Jiang Yiran, Zhang Heping
Department of Biostatistics, Yale University, New Haven, CT 06511.
Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2419721122. doi: 10.1073/pnas.2419721122. Epub 2025 Feb 25.
The genome-wide association studies identified genes associated with many diseases, but the identification and verification of disease variants are still challenging due to small effects and large number of individual variants. In this paper, we propose a powerful method that first quantifies the strength of regional associations at each single nucleotide polymorphism and converts these measures into time series data before using a change point detection algorithm to identify significant regions. In our extensive simulation study, the proposed method consistently demonstrates greater power than existing alternatives, achieving a relative increase of over 20% in challenging scenarios where true causal variants are sparse and multiple association regions exist at the same time, while maintaining a lower false positive rate.
全基因组关联研究确定了与许多疾病相关的基因，但由于效应较小且个体变异数量众多，疾病变异的识别和验证仍然具有挑战性。在本文中，我们提出了一种强大的方法，该方法首先量化每个单核苷酸多态性处的区域关联强度，并将这些测量值转换为时间序列数据，然后使用变化点检测算法来识别显著区域。在我们广泛的模拟研究中，所提出的方法始终显示出比现有方法更强的功效，在真实因果变异稀少且同时存在多个关联区域的具有挑战性的场景中，实现了超过20%的相对增幅，同时保持较低的假阳性率。
